MétaCan
Menu
Back to cohort

106 Spatial multi-omic characterization of tumor microenvironment heterogeneity in hepatocellular carcinoma

2025· article· W4416074415 on OpenAlexaff
Atefeh Khakpoor, Qanber Raza, Dina Kazemi, Erin Coll, Liang Lim, Nick Zabinyakov, Liang Qiao, Anna Di Bartolomeo, Helen M. McGuire, Jacob George, Ankur Sharma

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsTumor microenvironmentHepatocellular carcinomaTumor heterogeneityTumor cellsCancer

Abstract

fetched live from OpenAlex

Figure 2 Low and high abundance proteins were detected by IMC regardless of processing approach.IMC alone and post-Xenium IMC generate the same quality image, demonstrating highly consistent and reproducible data.For post-Xenium slides, the Xenium assay used was a custom panel using the Xenium v1 workflow

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.230
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRegular and Young Investigator Award AbstractsSame topicHepatocellular Carcinoma Treatment and PrognosisFrench-language works237,207